Are You Creating a Data Ready Enterprise?
In the old days, business intelligence was much easier. Enterprise data consisted largely of transactional data and while there were clear data issues in terms of data quality and integration, things were much simpler than they are today. In today’s world–as is captured effectively in the below slide—a data discussion is about a lot more things. And to truly make use of your enterprise data requires a lot more work than it did in the past.
Are we taking real advantage of all of our data?
In discussions with our customers, many say that they are failing to take advantage of the breath of today’s data. The question these customers are asking is where are we going wrong and how do they go forward and find a systematically way to unleash all of our data’s potential? Often an issue exists here because their data integration efforts have remained focused on the applications in their data centers or they have only collected structured, transactional data.
Additionally, we are seeing more than one connection point to each system using too many IT expert’s time or a bunch of different tools for different purposes. This cost the enterprise too much when software and personnel costs are totaled. And as the cloud has become popular and as Software-As-A-Service (SaaS) applications have entered the mainstream, many have continued to approach integration with just tools and more experts. In most organizations data integration consists of numerous individual projects, diverse technologies with little reuse and very slow turn-around times on new requests. Just overlaying another set of technologies on top of an already fragile set of infrastructure will not work.
Losing the data battle?
Put simply, continuing as described above will result in your enterprise losing rather than winning the data battle. The explosion of new data types is what makes change required. At the same time as new data types grow, more ways to use data with social, mobile, and devices. Our traditional approaches will not scale to support this change. We need to simplify and automate given the increasing number of data sources and the variety of data types the user needs. At the same time, if we continue to focus on the applications and not the data, we will be inundated with change management and not be able to focus on what is important, how to leverage the insights possible from all data sources.
The data ready enterprise
Obviously the above is not sustainable if we are going to win on data. The data ready enterprise masters their data landscape and manages data as an asset. They are enterprises where data is always on – where new requests do not require them to start from zero, but where great data is at the fingertips of every decision maker, every process, and every new app.
Imagine a single connection point between applications
What is needed is an enterprise analytics approach that creates a single connection point between applications and data sources. So how do we go forward and find a way to systematically unleash enterprise potential? One thing to do is focus on data before you build applications so you get the right data to solve the right enterprise problem. Additionally, use the advent of cloud and Software-As-A-Service style applications as an opportunity to eliminate hand coding and standardize on a data integration and quality solution. We cannot allow our data landscape to explode further as more types of data are added and more ways of using become social, mobile, and device data become possible. We need an approach that will truly scale.
What is needed is a next generation approach
How we can achieve these above ends? What is required is a next generation data platform. This platform needs to have two components. First is a data infrastructure layer that establishes a system of data integration across all apps, data warehouse, devices and processes – whether the data is in your data center, mobile or in the cloud. Anything that needs to be shared or consumed connects to the data infrastructure and the data infrastructure will ensure the correct connections, cleaning and mastering the data, and securing sensitive data.
Second is a data intelligence layer. This is about using data about the data and perhaps more importantly, use this metadata, to drive self-service, recommendations, guidance and automation. The system should manage all the data and enable the data consumer to search for available data. It should as well guide the user on additional ways to enrich or clean the data.
The amount of time we have in a day has not changed, yet we are asked to do so much more with our data. We will only succeed when we can automate the most mundane tasks by leveraging better data intelligence in the data management. And this is where innovation is needed – in the capability to do more with less.
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